Parameter estimation for metrology of features in an image

ABSTRACT

Methods and apparatuses are disclosed herein for parameter estimation for metrology. An example method at least includes optimizing, using a parameter estimation network, a parameter set to fit a feature in an image based on one or more models of the feature, the parameter set defining the one or more models, and providing metrology data of the feature in the image based on the optimized parameter set.

CROSS REFERENCE TO RELATED APPLICATION

The present application claims the priority benefit of U.S. PatentApplication Ser. No. 62/768,717, filed Nov. 16, 2018. The disclosures ofthe foregoing application are incorporated herein by reference

FIELD OF THE INVENTION

The invention relates generally to artificial intelligence (AI) enabledmetrology, and specifically to AI enhanced metrology for use on chargedparticle microscopy images.

BACKGROUND OF THE INVENTION

In many areas of industry and research, analysis and measurement ofsmall structures is performed for product/process development, qualitycontrol, medical evaluation, etc. Such analysis and measurement may beperformed using various types of inspection tools, which likely includeforming images of one or more structures of interest. For example, inthe semiconductor industry, charged particle microscopes are used toimage circuit structures on the nanometer scale, which typically becomethe basis for the analysis and measurement tasks. In such an example,measurements are performed on the images themselves to understandpotential for defects and process control. Such analysis andmeasurements, however, requires a highly skilled operator to determinewhere to measure and key features for use in performing themeasurements. This may typically be done using the creation of a recipethat can be ran once the key features are identified and located.

This identification and location of the key features by the skilledoperator, however, can be tedious and void of robustness. Additionally,small changes in imaging conditions or manufacturing processes mayrequire manually re-tuning the recipes due to the inability of therecipe to locate and/or identify the key features on its own. Suchrequirement to continually re-work the recipe due to changes in theimaging and/or the manufacturing makes full automation unreliable and/orunreachable. In many instances, the operators are required to screen outfalse positives to ensure the accuracy of the analysis. Removal of theconstant human interaction with the process is desirable in allindustries to increase productivity and reduce costs. Additionally, adesire for more robust automatic analysis and measurement of structures,especially small structures that experience changes in shape andconsistency, is desired.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 is an example of a charged particle microscope system inaccordance with an embodiment of the present disclosure.

FIG. 2 includes a number of example illustrations of features in TEMimages.

FIG. 3 is an example flow diagram of a parameter estimation network(PEN) in accordance with an embodiment disclosed herein.

FIG. 4 is an image sequence showing the change in model parameters shownby model-based images in accordance with an embodiment of the presentdisclosure.

FIG. 5 is an example image showing metrology of features in accordancewith an embodiment of the present disclosure.

FIG. 6 is an example functional block diagram of a computing system inaccordance with an embodiment of the present disclosure.

Like reference numerals refer to corresponding parts throughout theseveral views of the drawings.

DETAILED DESCRIPTION OF EMBODIMENTS

Embodiments of the present invention relate to AI enhanced metrology. Insome examples, the AI optimizes model parameters to fit the model tofeatures of interest, then the desired metrology data is obtained fromthe optimized model parameters. In some examples, the optimization ofthe model parameters is recursively performed by a regressionconvolutional neural network that receives an image of a feature andmodel images of the feature generated based on initial parameters, whichare then optimized through recursive application of the regressionconvolutional neural network. The optimized parameters then define amodel feature nearly exactly matching the feature in the image, andwhich provides desired metrology data. However, it should be understoodthat the methods described herein are generally applicable to a widerange of different AI enhanced metrology, and should not be consideredlimiting.

As used in this application and in the claims, the singular forms “a,”“an,” and “the” include the plural forms unless the context clearlydictates otherwise. Additionally, the term “includes” means “comprises.”Further, the term “coupled” does not exclude the presence ofintermediate elements between the coupled items. Additionally, in thefollowing discussion and in the claims, the terms “including” and“comprising” are used in an open-ended fashion, and thus should beinterpreted to mean “including, but not limited to . . . .” The term“integrated circuit” refers to a set of electronic components and theirinterconnections (internal electrical circuit elements, collectively)that are patterned on the surface of a microchip. The term“semiconductor device” refers generically to an integrated circuit (IC),which may be integral to a semiconductor wafer, separated from a wafer,or packaged for use on a circuit board. The term “FIB” or “focused ionbeam” is used herein to refer to any collimated ion beam, including abeam focused by ion optics and shaped ion beams.

The systems, apparatus, and methods described herein should not beconstrued as limiting in any way. Instead, the present disclosure isdirected toward all novel and non-obvious features and aspects of thevarious disclosed embodiments, alone and in various combinations andsub-combinations with one another. The disclosed systems, methods, andapparatus are not limited to any specific aspect or feature orcombinations thereof, nor do the disclosed systems, methods, andapparatus require that any one or more specific advantages be present orproblems be solved. Any theories of operation are to facilitateexplanation, but the disclosed systems, methods, and apparatus are notlimited to such theories of operation.

Although the operations of some of the disclosed methods are describedin a particular, sequential order for convenient presentation, it shouldbe understood that this manner of description encompasses rearrangement,unless a particular ordering is required by specific language set forthbelow. For example, operations described sequentially may in some casesbe rearranged or performed concurrently. Moreover, for the sake ofsimplicity, the attached figures may not show the various ways in whichthe disclosed systems, methods, and apparatus can be used in conjunctionwith other systems, methods, and apparatus. Additionally, thedescription sometimes uses terms like “produce” and “provide” todescribe the disclosed methods. These terms are high-level abstractionsof the actual operations that are performed. The actual operations thatcorrespond to these terms will vary depending on the particularimplementation and are readily discernible by one of ordinary skill inthe art.

In general, metrology on images, e.g., using images for a basis ofmeasurement, obtained with a charged particle microscope, for example,has conventionally required heavy user interaction to obtain qualitydata. The heavy user interaction may be required because size, shape,noise, and contrast variations may otherwise result in failed orincorrect measurements. While metrology is a required aspect of processcontrol and defect detection in certain industries, such as themicroelectronics industry, improvements in image recognition andmetrology are greatly desired irrespective of the industry. It should benoted that while the discussion herein uses the microelectronicsindustry and circuit/component structures to illustrate the disclosedtechniques, the use of the microelectronics industry is not limiting andthe disclosed techniques may be implemented on images of any kind forany measurement purposes without exceeding the bounds of the disclosure,and all current and future uses are contemplated herein.

One solution to the above disclosed problem includes a parameterestimation network (PEN) that aligns a feature model to the referenceimage through a recurrent convolutional neural network. The parameterestimation network differs from facial recognition in that a model isprovided to fit a variety of different features. As such, the featuremodel is an input into PEN. This feature model is customized per featureand then trained on the PEN. Secondly, TEM metrology 1σ measurementprecision less than 0.1% of the image resolution is desired to becomparable to current TEM metrology. To achieve this sizeableimprovement, PEN includes improvements in model definition, training,and recursion compared with other methods. The parameter estimationnetwork is designed to fit model parameters to a supplied image, whichthen also provides the desired metrology data.

FIG. 1 is an example of a charged particle microscope system 100 inaccordance with an embodiment of the present disclosure. The chargedparticle microscope (CPM) system 100, or simply system 100, at leastincludes a CPM environment 102, a network 104, one or more servers 106,and a parameter estimation network (PEN) 114. The CPM system 100 may beused to investigate and analyze samples of various size and makeup. Forone example, the CPM system 100 may be implemented, at least partially,at an integrated circuit manufacturing site and used to analyze andmeasure various aspects of wafers and circuits fabricated at the site.In some embodiments, the CPM system 100 may be distributed acrossvarious locations. For example, the CPM environment 102 may be locatedat a fabrication or development location, the network 104 distributedlocally, regionally, or nationally, and the server 106 located at aserver farm and coupled to the CPM environment 100 via the network 104.Regardless of the organization of the CPM system 100, the system 100 mayat least be used to implement one or more PENs 114 to perform variousmetrology-related tasks.

The CPM environment 102 includes any type of charged particlemicroscope, but the application of the neural network and analyticsdisclosed herein is not limited to charged particle microscopy, which isused for illustrative purposes only. Example CPMs include scanningelectron microscopes (SEMs), transmission electron microscopes (TEMs),scanning transmission electron microscopes (STEMs), focused ion beams(FIBs), and dual beam (DB) systems that include both electron and ionbeam capabilities, to name a few. The CPM environment 102 may be used toobtain electron or ion images of samples, some of which may be thinsections, e.g., lamellae, taken from a larger sample or wafer. The CPMenvironment 102 may include various aspects that can be contained in asingle tool or that may be situated in separate tools. For example, theCPM environment 102 may include an imaging platform 108, e.g., an SEM,TEM, or STEM, a sample preparation platform 110, and one or morecontrollers 112. Of course, each platform 108 and 110 may include morethan one microscope/sample preparation tools as well.

The imaging platform 108 is used to obtain images of samples, some ofthe samples may have been prepared by the sample prep platform 110, butthat is not necessary. The images are obtained using an electron and/orion source to irradiate the sample with a respective beam of chargedparticles. In some examples, the charged particle beam imaging isobtained by a scanned beam, e.g., moved across the sample, while otherexamples the charged particle beam is not scanned. Backscattered,secondary, or transmitted electrons, for example, are then detected andgray scale images formed based thereon. The images include gray scalecontrast depending on the materials of the sample, where the changes ingray scale indicate changes in the material type and/or crystalorientation. The imaging platform 108 may be controlled by internalcontrols (not shown), controller 112, or a combination thereof.

The sample prep platform 110 forms some of the samples that are imagedby the imaging platform 108. Of course, imaged samples may also beformed by other tools (not shown). The sample prep 110 may, for example,be a DB system that uses a FIB to prepare and assist in the removal of athin sample from a larger sample, such as by ion milling, ion inducedetching, or a combination thereof, and other processes to process thesample for imaging. Other processes may include, but are not limited to,planarizing mills/etches, fiducial generation, cross-section formation,top-down lamella preparation, etc. The sample prep platform 110 may alsoinclude an electron imaging component that allows the sample prepprocess to be monitored, but the electron imaging component is notrequired. In some embodiments, the sample prep platform 110 may includeother physical preparation aspects—lasers, cutting tools, resinencapsulation tools, cryogenic tools, etc.—that are used to prepare thesample for the imaging platform 108. The sample prep platform 110 may becontrolled by internal controls (not shown), controller 112, or acombination thereof.

The network 104 may be any kind of network for transmitting signalsbetween the CPM environment 102 and the server(s) 106. For example, thenetwork 104 may be a local area network, a large area network, or adistributive network, such as the internet, a telephony backbone, andcombinations thereof.

The servers 106 may include one or more computing platforms, virtualand/or physical, that can run code for various algorithms, neuralnetworks, and analytical suites. While not shown, a user of the CPMenvironment 102 may have access to the servers 106 for retrieval ofdata, updating software code, performing analytical tasks on data, etc.,where the access is through the network 104 from the user's localcomputing environment (not shown). In some embodiments, the useraccesses image data stored on the servers 106, implements segmentationand regression tasks using the PEN 114 (which may be executed on theservers 106 or the CPM Environment 102), and performs metrology at theirlocal computing environment. In some embodiments, the segmentation andregression tasks may be performed in conjunction with model generatingtasks performed by algorithms 116.

In operation, one or more images of a sample are obtained by the imagingplatform 108. At least one of the images, which includes one or morestructures of interest for example, may be analyzed by parameterestimation network (PEN) 114 to provide metrology data on the one ormore structures of interest. The PEN 114 may be included with CPMenvironment 102, servers 106, or a combination thereof. The metrologydata is determined by PEN 114 based on the recursive application of aregression convolutional neural network to an image of a feature ofinterest and one or more models of the feature of interest. The one ormore models may be initially generated by based on an initial parameterset, which is step-wise optimized by comparing the one or more models tothe feature in the image, then adjusting the parameter set so that themodel converges to the feature of interest. Once the parameter set isoptimized, then the metrology data is provided based on the model, notthe image. Convergence of the model/parameter set may be determinedbased on a pixel step size between iterations falling below a pixel stepthreshold, such as 0.5 pixels, or based on a threshold number ofiterations performed, 12 for example. The thresholds, however, are anon-limiting aspect of the present disclosure and any user may definetheir desired thresholds.

More specifically, and as shown in FIG. 3 , an initial parameter set P₀is provided to a model generation function of the PEN 114, which maygenerate a region model image and a boundary model image, which are thenprovided to a regression convolutional neural network (RCNN) along withan image of one or more features. The RCNN may segment the image andextract a feature of interest. In some embodiments, reorientation of thefeature and resizing the image to match the size of the model images mayalso occur. Once the feature of interest is extracted, the RCNN comparesthe segmented image of the feature of interest to the region andboundary model images, which may be done at the pixel level, but is notrequired. The differences between the image and the model images arethen output and combined (e.g., added, subtracted, multiplied, divided)with the initial parameters to generate updated parameter set.Subsequently, based on the updated parameter set, the process may berepeated to further update (e.g., optimize) the parameter set, e.g., P₁.After each iteration of the process, the pixel step size, e.g., changein pixels of the model images, is compared against a threshold as notedabove. Additionally, the number of iterations are compared to athreshold. If either of the comparisons have met or exceeded theirrespective threshold, the iterations are stopped and metrology databased on the optimized parameter set are provided.

While the image provided to the PEN 114 is described as being obtainedby imaging platform 108, in other embodiments, the image may be providedby a different imaging platform and provided to the PEN 114 via thenetwork 104.

In one or more embodiments, the RCNN of the PEN 114 may be referred toas a deep learning system or a machine-learning computing system. TheRCNN of PEN 114 includes a collection of connected units or nodes, whichare called artificial neurons. Each connection transmits a signal fromone artificial neuron to another. Artificial neurons may be aggregatedinto layers. Different layers may perform different kinds oftransformations on their inputs.

An RCNN is conventionally designed to process data that come in the formof multiple arrays, such as a color image composed of threetwo-dimensional arrays containing pixel intensities in three colorchannels. Example architecture of an RCNN is structured as a series ofstages. The first few stages may be composed of two types of layers:convolutional layers and pooling layers. A convolutional layer applies aconvolution operation to the input, passing the result to the nextlayer. The convolution emulates the response of an individual neuron tovisual stimuli. A pooling layer combines the outputs of neuron clustersat one layer into a single neuron in the next layer. For example, maxpooling uses the maximum value from each of a cluster of neurons at theprior layer. Following the convolutional layers and pooling layers willbe one or more fully connected regression layers.

In one or more embodiments, the RCNN is configured to detect and/oridentify, e.g., classify, objects of interest shown in an input image ofa sample and compare multiple images to determine differencestherebetween. An object of interest is a portion of the sample that isunder study. The remaining portions of the specimen provide context forthe object of interest. However, the object of interest needs to bemeasured while the remaining portions of the specimen may be ignored. Asan example, one or more round, oval, pillar-like and/or layeredstructures may be objects of interest within an image, and the one ormore components may be measured or measurements determined from anoptimized model. Of course, the objects of interest disclosed herein arefor illustrative purposes only, and any type of object of interestcaptured by a CPM system 100 may be measured by the PEN 114.

Prior to use, the PEN 114 may need to be trained to identify desiredfeatures of a structure in an image. Stated another way, the PEN 114needs to learn how to segment images as desired and compare thesegmented images to the model images. The training may typically includeproviding the PEN 114, or more precisely the RCNN, a number of annotatedimages with the annotations highlighting desired/different components ofthe structure. For example, boundaries and key reference points may behighlighted. The training images may typically include images of variousquality and also include structures of various conformity with a desiredshape. Based on the training images, PEN 114 learns how to identify thevarious classes of any image received regardless of image/structurequality. Further, the amount of training images may be based on thecomplexity of the structures being analyzed, with less complexityrequiring fewer training images.

FIG. 2 includes a number of example illustrations 200 of features in TEMimages. The images in FIG. 2 provide examples of the variations in bothfeature shape/size and image quality, as discussed above and can affectrobust automated metrology. Illustrations 200 include images 220Athrough 220D, with each image 220 showing respective features 222 and224. The features 222A-D and 224A-D may be features of interest anddesired metrology information, such as width of feature 222 at variouslocations, the height of feature 222, and thickness of feature 224, isobtained through the techniques disclosed herein.

As can be seen in FIG. 2 , each image 220A-D is of a different quality,such as contrast, sharpness, texture, and noise, even after they havebeen contrast normalized. The images 200, after being contrastnormalized, are provided to a parameter estimation network, such as PEN114, so that the desired metrology data may be automatically androbustly determined. Without the use of the PEN, the wide variation inimage quality and feature shape/size may require a user to manuallyverify measurement locations and data even when using conventionalautomated measurement techniques.

FIG. 3 is an example flow diagram of a parameter estimation network(PEN) 314 in accordance with an embodiment disclosed herein. The PEN314, which may be an example of PEN 114, receives an initial image and aplurality of model parameters, e.g., a parameter set, as inputs forperforming metrology on one or more features included in the image. ThePEN 314 may be performed by a computing system or a charged particleenvironment system, such as system 100 for example, to providemeasurements, e.g., metrology data, on desired features in one or moreimages. In some embodiments, the images may be TEM images ofnanometer-scaled features of integrated circuit components, but that isnot limiting on the present disclosure. The input images may not needany pre-processing but may at least be normalized for contrast and sizedfor the PEN 314.

PEN 314 may begin at process block 330, which generates a model ormodels of the features of interest based on an initial parameter set P₀.The initial parameter set P₀ can include any number of desiredparameters that define the features of interest. In the embodiment ofmethod 300, P₀ includes 17 parameters that define the model shown inmodel images 332 and 334. Model image 332 shows boundaries of thefeatures of interest, such as the squat pillar 222 and the sub-layer224. The parameter space may be based on geometric models, statisticalmodels, or any model that provides for parametrization of the feature(s)of interest. The initial parameters P₀ may describe the features using atarget size/shape based on CAD data, for example, or may be based on aheuristically generated parameters from previous measurements. The modelgeneration block 330 generates the region model 334, which defines thedifferent regions of the features and background. The region model 334shows the squat pillar 222 in green, sub-layer 224 in red and backgroundin blue, is the background being anything that is not either the squatpillar 222 or the sub-layer 224. Additionally, the model generationblock 330 generates the boundary model 332 that shows the boundaries ofthe features of interest, such as the squat pillar and sub-layer.

Process block 336 receives the two models, region model 334 and boundarymodel 332, along with the image 320 of the features. The process block336 may be a regression convolutional neural network (RCNN) thatincludes a number of convolutional nodes followed by a number of fullyconnected layers, e.g., regression layers. In general, process block 336may localize features in the image 320 using segmentation techniques,e.g., FCN techniques, to identify the features of interest in image 320,along with optimizing the parameters P₀ of the model, fitting the modelimages to the feature, which provides error/deviation of the feature inthe image to the initial parameters P₀. Additionally, process block 336may re-orient the features in the image 320, if necessary, and crop outthe features.

The process block 336 compares the region and boundary model images 334,332 to the image 320 and determines how the parameters should beadjusted to provide a better fit of the model to the features in image320. The error/adjustment amount is then provided to combiner 338, whichalso receives the initial parameters P₀. The combiner 338 then providesupdated parameter set P₁ based on how the error/adjustment amountchanges the parameter set P₀.

The updated parameter set P₁ is then provided to process block 340,which generates region and boundary model images 344 and 342,respectively, based on parameter set P₁.

The process block 346 then performs the parameter estimation processsimilar to process block 336 to further optimize the parameter set.Combiner 348 then compares the further optimized parameters provided byprocess block 346 with parameter set P₁ to provide update parameter setP₂.

The process may then repeat until the difference between the output of aRCNN block and the updated parameters is less than a threshold pixelsize, for example. Alternatively, the PEN 314 may iterate until athreshold number of iterations have been performed, for example. Oncethe PEN 314 has ended, the final parameter set may be used to define thefeatures. The defined features are then used to provide the metrologydata on those features. As such, the model based on the updatedparameter set is the basis of the metrology data.

FIG. 4 is an image sequence 400 showing the change in model parametersshown by model-based images in accordance with an embodiment of thepresent disclosure. The image sequence 400 shows the evolution of modelparameters as optimized by the PEN 314, for example. The image sequence400 shows the change in the regional and boundary model images inrelation to a TEM image as their respective parameter sets change due toiterations by the PEN 314. The graph 401 shows the change in pixel stepsize, e.g., change in error between the image and the models, periteration of the PEN 314.

Image 420A shows features 422 and 424 (green part of the image), squatpillar and sub-layer, respectively, along with boundary model image 432(blue line), and region model image 434 (red part of image, whichappears orange and yellow due to the green image color). See images 332and 334 for example boundary and region model images. The boundary andregion model images 432 and 434 are based on an initial, genericparameter set P0, as discussed above, and likely do not match thefeatures 422 and 424 of the image 420A. The mismatch is illustrated bythe red region 405, which shows a difference in the feature 422 and thesquat pillar portion of the region model 434. Additionally, the boundarymodel 432 does not sit on the feature boundaries as can be seen by thered area and the areas 407 above and below feature 424. Further, andmaybe harder to highlight, is the top of feature 422 extending aboveboundary 432. The RCNN 336 of PEN 314 identifies the mismatch andprovides a new parameter set P1 to provide a better match between themodels 432, 434 and the features 422, 424 of image 420A.

Image 420B shows the new models 432, 434 and their changes with respectto the features 422, 424. As can be seen in image 420B, the red areabetween the squat pillar 422 and the boundary 432 has decreased, and theboundary 432 has become more align with the top edges of the squatpillar 422 including the mushroom-like portions. Additionally, themismatch areas 407 have decreased as well with the region model 434moving up to be more in line with the sub-layer 424. Again, the RCNN 336of PEN 314 identifies the mismatch and provides a new parameter set P2to provide a better match between the models 432, 434 and the features422, 424 of image 420B.

Images 420C through 420E show further refinement/optimization of the newmodels 432, 434 and their changes with respect to the features 422, 424.Image, 420E shows, at least to the human, models matched to the featuresof interest.

As can be seen in graph 401, as iterations of PEN 314 are performed, thepixel step size based on L2 norm becomes sub-pixel changes between themodels and the image after 6 iterations. This implies that thedifferences between the models and the image are less than a pixel sizeand results in high precision metrology of the features based on theobtain model parameters Pn.

FIG. 5 is an example image 500 showing metrology of features inaccordance with an embodiment of the present disclosure. The image 500includes metrology data of squat pillar and sub-layer features 522 and524, respectively. The data is based on an optimized parameter set asprovided by PEN 314, for example. The metrology data, which is alsodetermined by PEN 314, includes a height 552 of the squat pillar, aheight 554 of the squat pillar plus the sub-layer, and a widths of thesquat layer at various height locations. For example, a top width 556, amiddle width 558 and a bottom width 560 may be determined. While fourmeasurements are shown in image 500, the number and location ofmeasurements may be any desired.

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs), fieldprogrammable gate arrays (FPGAs), or network processing units (NPUs)that are persistently programmed to perform the techniques, or mayinclude one or more general purpose hardware processors or graphicsprocessing units (GPUs) programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, FPGAs, or NPUs with custom programmingto accomplish the techniques. The special-purpose computing devices maybe desktop computer systems, portable computer systems, handhelddevices, networking devices or any other device that incorporateshard-wired and/or program logic to implement the techniques.

For example, FIG. 6 is a block diagram that illustrates a computersystem 600 upon which an embodiment of the invention may be implemented.The computing system 600 may be an example of the computing hardwareincluded with CPM environment 102, such a controller 112, imagingplatform 108, sample preparation platform 110, and/or servers 106.Additionally, computer system 600 may be used to implement the one ormore neural networks disclosed herein, such as PEN 114 and/or RCNN336/346. Computer system 600 at least includes a bus 640 or othercommunication mechanism for communicating information, and a hardwareprocessor 642 coupled with bus 640 for processing information. Hardwareprocessor 642 may be, for example, a general purpose microprocessor. Thecomputing system 600 may be used to implement the methods and techniquesdisclosed herein, such as methods 301 and 401, and may also be used toobtain images and segment said images with one or more classes.

Computer system 600 also includes a main memory 644, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 640for storing information and instructions to be executed by processor642. Main memory 644 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 642. Such instructions, when stored innon-transitory storage media accessible to processor 642, rendercomputer system 600 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 600 further includes a read only memory (ROM) 646 orother static storage device coupled to bus 640 for storing staticinformation and instructions for processor 642. A storage device 648,such as a magnetic disk or optical disk, is provided and coupled to bus640 for storing information and instructions.

Computer system 600 may be coupled via bus 640 to a display 650, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 652, including alphanumeric and other keys, is coupledto bus 640 for communicating information and command selections toprocessor 642. Another type of user input device is cursor control 654,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 642 and forcontrolling cursor movement on display 650. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 600 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 600 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 600 in response to processor 642 executing one or more sequencesof one or more instructions contained in main memory 644. Suchinstructions may be read into main memory 644 from another storagemedium, such as storage device 648. Execution of the sequences ofinstructions contained in main memory 644 causes processor 642 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 648.Volatile media includes dynamic memory, such as main memory 644. Commonforms of storage media include, for example, a floppy disk, a flexibledisk, hard disk, solid state drive, magnetic tape, or any other magneticdata storage medium, a CD-ROM, any other optical data storage medium,any physical medium with patterns of holes, a RAM, a PROM, and EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge,content-addressable memory (CAM), and ternary content-addressable memory(TCAM).

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 640. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 642 for execution. For example,the instructions may initially be carried on a magnetic disk or solidstate drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 600 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 640. Bus 640 carries the data tomain memory 644, from which processor 642 retrieves and executes theinstructions. The instructions received by main memory 644 mayoptionally be stored on storage device 648 either before or afterexecution by processor 642.

Computer system 600 also includes a communication interface 656 coupledto bus 640. Communication interface 656 provides a two-way datacommunication coupling to a network link 658 that is connected to alocal network 660. For example, communication interface 656 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 656 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 656sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 658 typically provides data communication through one ormore networks to other data devices. For example, network link 658 mayprovide a connection through local network 660 to a host computer 662 orto data equipment operated by an Internet Service Provider (ISP) 664.ISP 664 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 666. Local network 660 and Internet 666 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 658and through communication interface 656, which carry the digital data toand from computer system 600, are example forms of transmission media.

Computer system 600 can send messages and receive data, includingprogram code, through the network(s), network link 658 and communicationinterface 656. In the Internet example, a server 668 might transmit arequested code for an application program through Internet 666, ISP 664,local network 660 and communication interface 656.

The received code may be executed by processor 642 as it is received,and/or stored in storage device 648, or other non-volatile storage forlater execution.

In some examples, values, procedures, or apparatuses are referred to as“lowest”, “best”, “minimum,” or the like. It will be appreciated thatsuch descriptions are intended to indicate that a selection among manyused functional alternatives can be made, and such selections need notbe better, smaller, or otherwise preferable to other selections. Inaddition, the values selected may be obtained by numerical or otherapproximate means and may only be an approximation to the theoreticallycorrect/value.

What is claimed is:
 1. A method comprising: generating a regional modelimage of a feature based on first model parameters; generating aboundary model image of the feature based on the first model parameters;optimizing, using a parameter estimation network, the model parametersbased on comparing the regional and boundary model images to a featureof interest in an image, wherein optimizing, using a parameterestimation network, the model parameters based comparing the regionaland boundary model images to a feature of interest in an image includes:segmenting, using the parameter estimation network, the image to extractthe feature of interest; comparing, using the parameter estimationnetwork, the extracted feature of interest to region and boundary modelsgenerated based on a first parameter set; based on the comparison,determining, by the parameter estimation network, changes to the firstparameter set so that the region and boundary model images match thefeature of interest; and updating the first parameter set based on thedetermined changes; and providing one or more feature dimensions basedon the optimized model parameters.
 2. The method of claim 1, whereinoptimizing, using a parameter estimation network, the model parametersbased comparing the regional and boundary model images to a feature ofinterest in an image includes: iteratively updating, using the parameterestimation network, the model parameters based on comparing updatedregion and boundary model images to the feature of interest in theimage, wherein the updated region and boundary model images are based ona parameter set updated in a previous iteration.
 3. The method of claim1, wherein the parameter estimation network includes a regressionconvolutional neural network.
 4. The method of claim 1, wherein thesteps of comparing, determining and updating are performed until eithera threshold number of iterations are met or a threshold step sizebetween iterations is met.
 5. The method of claim 1, wherein generatinga boundary image of the feature based on the first model parametersincludes: generating a pixel mask image, the pixel mask image defining atype and location of the feature.
 6. The method of claim 1, whereinproviding one or more feature dimensions based on the optimized modelparameters includes: determining a truncated distance weighting for eachpixel from a feature boundary; and generating the boundary image basedon the truncated distance weighting.
 7. The method of claim 1, whereinthe first parameters are initial parameters.
 8. The method of claim 1,wherein the first parameters are updated parameters.
 9. A methodcomprising: optimizing, using a parameter estimation network, aparameter set to fit a feature in an image based on one or more modelsof the feature, the parameter set defining the one or more models,wherein optimizing, using a parameter estimation network, a parameterset to fit a feature in an image includes: using a regressionconvolutional neural network, segmenting the image to extract thefeature in the image; and using the regression convolutional neuralnetwork, fitting the parameter set to the feature in the image tooptimize the parameter set; and providing metrology data of the featurein the image based on the optimized parameter set.
 10. The method ofclaim 9, wherein optimizing, using a parameter estimation network, aparameter set to fit a feature in an image includes: comparing, by aregression convolutional neural network, the one or more feature modelsformed based on the parameter set to the feature in the image; and basedon differences between the one or more feature models and the feature inthe image, updating the parameter set to provide an optimized parameterset.
 11. The method of claim 10, wherein updating the parameter set toprovide an optimized parameter set includes iteratively adjusting theparameter set so that the one or more models converge to match thefeature in the image.
 12. The method of claim 9, wherein optimizing,using a parameter estimation network, a parameter set to fit a featurein an image based on one or more models of the feature includes:recursively updating the parameter set based on a difference between theone or more models generated on an updated parameter set and the featurein the image until convergence is obtained.
 13. The method of claim 12,wherein convergence is determined either when a threshold number ofiterations has been met, or when a step size of parameter set change isbelow a threshold number of pixels.
 14. The method of claim 9, furtherincluding aligning the extracted feature to the one or more models. 15.The method of claim 9, further including generating a region model ofthe feature based on the parameter set, wherein the region model is apixel mask image defining a type and location of the feature.
 16. Themethod of claim 9, further including generating a boundary model of thefeature based on the parameter set, wherein each pixel in the boundarymodel is a truncated distance weighting from a feature boundary.
 17. Themethod of claim 9, wherein providing metrology data of the feature inthe image based on the optimized parameter set includes: generating amodel of the feature based on the optimized parameter set, wherein thegenerated model determines the metrology data.
 18. The method of claim9, wherein the one or more models includes a region model image and aboundary model image.